HMM Quantifying investor sentiment: Research on sentiment factors based on text analysis and Hidden Markov Model-Reference-Cited by-同舟云学术

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HMM Quantifying investor sentiment: Research on sentiment factors based on text analysis and Hidden Markov Model

Published:2024-01-19 Issue: Volume:31 Page:635-640
ISSN:
Container-title:Proceedings of the 2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence
language:
Short-container-title:

Author:

Zhao YanRu¹^ORCID,Chen Die²^ORCID

Affiliation:

1. Department of Statistics, University of Electronic Science and Technology, China

2. College of Mathematical Sciences, University of Electronic Science and Technology, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3675417.3675522

Reference9 articles.

1. S. L. Heston, N. R. Sinha, News versus sentiment: Comparing textual processing approaches for predicting stock returns, Robert H. Smith School Research Paper, 2014, 13-16.

2. M. Xuejing M. Xianglan and H. Yangyang. 2016. Research on Investor Sentiment Index Based on Text Mining and Baidu Index. Macroeconomic research (1) 10:144-153.

3. Hidden Markov model prediction of the stock market based on investor sentiment;Xuan Y.;Diss. Huaqiao University,2017

4. Predicting the effect of Googling investor sentiment on Islamic stock market returns

5. Stock price prediction based on discrete hidden Markov model;Xudong Z.;Journal of Zhejiang University of Technology (2),2020

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